Empirical Studies in Decision Rule-Based Flexibility Analysis for Complex Systems Design and Management

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This paper presents the results of human subject experiments focusing on the role of decision rules in the study of flexibility and real options analysis (ROA) in design and management of complex engineering systems. Decision rules are heuristics-based triggering mechanisms that help determine the ideal conditions for exercising flexibility in system operations. In contrast to standard ROA based on dynamic programming, decision rules can be parameterized as decision variables, and therefore capture the decision-making process based on specific realizations of the main uncertainty drivers affecting system performance. Similar to standard ROA, a decision rule approach can be used to quantify the benefits of flexibility in early conceptual design studies, and help identifying the best flexible systems design concepts before a more detailed design phase. While many studies demonstrate expected lifecycle performance improvement stemming from a decision-rule based approach as compared to standard design and ROA techniques, very few studies show experimentally their effectiveness in managing flexible engineering systems. This paper presents the results of controlled human-subject experiments involving thirty-two participants evaluating a training procedure in a simulation game environment. The controlled study show that a stochastically optimal flexible strategy combined with an initial policy for the system configuration can improve significantly the expected coverage rate of medical emergencies. These provide insights for further research, development and evaluation of flexible systems design and management strategies for complex engineering systems.